Update README.md

README.md

@@ -82,24 +82,6 @@ You can view other LaMini model series as follow. Note that not all models are p
 </tbody>
 </table>
 
-## Training Procedure
-We initialize with [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) and fine-tune it on our [LaMini dataset](). Its total number of parameters is 61M. 
-
-### Training Hyperparameters
-
-The following hyperparameters were used during training:
-- learning_rate: 0.0005
-- train_batch_size: 128
-- eval_batch_size: 64
-- seed: 42
-- gradient_accumulation_steps: 4
-- total_train_batch_size: 512
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- num_epochs: 5
-
-## Evaluation
-We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). 
 
 ## Use
 
@@ -122,6 +104,25 @@ generated_text = generator(input_prompt, max_length=512, do_sample=True)[0]['gen
 print("Response": generated_text)
 ```
 
+## Training Procedure
+We initialize with [google/flan-t5-small](https://huggingface.co/google/flan-t5-small) and fine-tune it on our [LaMini dataset](). Its total number of parameters is 61M. 
+
+### Training Hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 0.0005
+- train_batch_size: 128
+- eval_batch_size: 64
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 512
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 5
+
+## Evaluation
+We conducted two sets of evaluations: automatic evaluation on downstream NLP tasks and human evaluation on user-oriented instructions. For more detail, please refer to our [paper](). 
+
 ## Limitations
 
 More information needed